Many approaches have been presented in recent years to address the pose estimation problem for texture-less objects with only RGB images. Due to the lack of appearance features, traditional methods usually tackle the problem via holistic template matching techniques [1]}, [2]}, [3]}, but are susceptible to scale change and cluttered environments. More recently, deep learning techniques, such as convolutional neural networks (CNNs), have been employed to overcome these challenges. As two pioneering methods, SSD-6D [4]} and PoseCNN [5]} developed the CNN architectures to estimate the 6D object poses from a single RGB image. In comparison, some recent works leverage CNNs to first predict 2D object keypoints [6]}, [7]}, [8]} or dense 2D-3D correspondences [9]}, [10]}, [11]}, and then compute the pose through 2D-3D correspondences with a PnP algorithm [12]}. Although these methods show good 2D detection results, they generally have low accuracy in the final 6D poses.
